Background. Niemann-Pick Disease (NPD) type B is a rare autosomal recessive disease characterised by hepatosplenomegaly and pulmonary disease, highlighted by preserved volumes and diminished diffusion capacity of the lung for carbon monoxide (DLCO) on pulmonary function tests (PFTs). There is no current accepted treatment for the disease. We present a case of a successful bilateral lung transplant in a patient with a DLCO of 14%, and significant pulmonary changes attributable to NPD type B on computed tomography (CT) chest, and both microscopic and macroscopic assessment of the lung explant. To the author’s knowledge this is only the third case of lung transplantation in a patient with NPD type B and is one of two current living patients post lung transplantation for NPD type B. Case Report. A 64-year-old male patient underwent bilateral lung transplantation for NPD type B. Preoperative PFTs demonstrated preserved volumes with significantly decreased DLCO, with imaging showing a diffuse reticular interstitial pattern, typical of chronic fibrotic lung disease. The patient suffered from primary graft dysfunction type 3 in the postoperative period as well as rejection managed with methylprednisolone and intravenous immunoglobulin. The patient improved steadily and was discharged 80 days post-transplantation. Conclusions. This case is only the third reported case of lung transplantation in a patient with NPD type B and the second case of a patient with NPD type B currently living post-transplantation, being at postoperative day (POD) 267 at the time of manuscript drafting. It demonstrates that lung transplantation, although hazardous, is a viable strategy for treatment in patients with NPD type B who have significant pulmonary involvement.
BackgroundChronic lung allograft dysfunction (CLAD) is the major cause of death post-lung transplantation, with acute cellular rejection (ACR) being the biggest contributing risk factor. Although patients are routinely monitored with spirometry, FEV1 is stable or improving in most ACR episodes. In contrast, oscillometry is highly sensitive to respiratory mechanics and shown to track graft injury associated with ACR and its improvement following treatment. We hypothesize that intra-subject variability in oscillometry measurements correlates with ACR and risk of CLAD.MethodsOf 289 bilateral lung recipients enrolled for oscillometry prior to laboratory-based spirometry between December 2017 and March 2020, 230 had ≥ 3 months and 175 had ≥ 6 months of follow-up. While 37 patients developed CLAD, only 29 had oscillometry at time of CLAD onset and were included for analysis. These 29 CLAD patients were time-matched with 129 CLAD-free recipients. We performed multivariable regression to investigate the associations between variance in spirometry/oscillometry and the A-score, a cumulative index of ACR, as our predictor of primary interest. Conditional logistic regression models were built to investigate associations with CLAD.ResultsMultivariable regression showed that the A-score was positively associated with the variance in oscillometry measurements. Conditional logistic regression models revealed that higher variance in the oscillometry metrics of ventilatory inhomogeneity, X5, AX, and R5-19, was independently associated with increased risk of CLAD (p < 0.05); no association was found for variance in %predicted FEV1.ConclusionOscillometry tracks graft injury and recovery post-transplant. Monitoring with oscillometry could facilitate earlier identification of graft injury, prompting investigation to identify treatable causes and decrease the risk of CLAD.
RationaleSpirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretation of spirometry alone.MethodsWe built a multilayer perceptron model using full PFTs from 748 patients, interpreted according to international guidelines. Inputs included spirometry (forced vital capacity, forced expiratory volume in 1 s, forced mid-expiratory flow25–75), plethysmography (total lung capacity, residual volume) and biometrics (sex, age, height). The model was developed with 2582 PFTs from 477 patients, randomly divided into training (80%), validation (10%) and test (10%) sets, and refined using 1245 previously unseen PFTs from 271 patients, split 50/50 as validation (136 patients) and test (135 patients) sets. Only one test per patient was used for each of 10 experiments conducted for each input combination. The final model was compared with interpretation of 82 spirometry tests by 6 trained pulmonologists and a decision tree.ResultsAccuracies from the first 477 patients were similar when inputs included biometrics+spirometry+plethysmography (95%±3%) vs biometrics+spirometry (90%±2%). Model refinement with the next 271 patients improved accuracies with biometrics+pirometry (95%±2%) but no change for biometrics+spirometry+plethysmography (95%±2%). The final model significantly outperformed (94.67%±2.63%, p<0.01 for both) interpretation of 82 spirometry tests by the decision tree (75.61%±0.00%) and pulmonologists (66.67%±14.63%).ConclusionsDeep learning improves the diagnostic acumen of spirometry and classifies lung physiology better than pulmonologists with accuracies comparable to full PFTs.
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